{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘制电梯加速度曲线图\n",
    "\n",
    "我发现了一款名为 [Sensor Kinetics](https://itunes.apple.com/us/app/sensor-kinetics-pro/id623633248?mt=8) 的iPhone应用程序，它可以让你直接访问手机的IMU数据，包括加速度计、速率陀螺仪和磁力计的数据。\n",
    "\n",
    "我把手机放在优达学城电梯的地板上，点击“开始收集数据”。\n",
    "\n",
    "然后，我按下了三楼的电梯按钮，并在电梯移动到三楼时让应用程序收集数据。\n",
    "\n",
    "下面，我要向你展示这些数据的一个曲线图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports and getting the data from the CSV\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "data = np.genfromtxt(\"elevator-lac.csv\", delimiter=\",\")[100:570]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unpack that data\n",
    "\n",
    "t, a_x, a_y, a_z = data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the graph\n",
    "plt.ylabel(\"Vertical Acceleration (m/s/s)\")\n",
    "plt.xlabel(\"Time (seconds)\")\n",
    "plt.plot(t,a_z+0.12) # the \"+0.12\" is to account for bias in the data\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这是来自我手机加速度计的真实加速度数据，这些数据讲述了一个故事。在你进行下一步之前，我只想让你思考下列几个问题：\n",
    "\n",
    "### 思考题\n",
    "\n",
    "1. 在t = 6和t = 9之间的第一个波峰表示什么含义？它对应的电梯运动的哪一部分？\n",
    "\n",
    "2. t = 9和t = 16之间最平坦的部分，表示什么含义？\n",
    "\n",
    "3. 最后一个问题，为什么最后一个波峰出现在t = 16和18之间？电梯运动的哪些部分与之相对应？\n",
    "\n",
    "### 在本课结束时......\n",
    "你将能够：\n",
    "\n",
    "1. 使用这样的数据来确定电梯在任意时刻移动的**速度**。\n",
    "\n",
    "2. 使用这样的数据来计算这次活动中电梯移动的**距离**。"
   ]
  }
 ],
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   "language": "python",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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  "toc": {
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